Overview

Dataset statistics

Number of variables12
Number of observations1000
Missing cells192
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory74.3 KiB
Average record size in memory76.1 B

Variable types

Numeric7
Categorical5

Warnings

title has a high cardinality: 999 distinct values High cardinality
genre has a high cardinality: 207 distinct values High cardinality
description has a high cardinality: 1000 distinct values High cardinality
director has a high cardinality: 644 distinct values High cardinality
actors has a high cardinality: 996 distinct values High cardinality
revenue (millions) has 128 (12.8%) missing values Missing
metascore has 64 (6.4%) missing values Missing
rank is uniformly distributed Uniform
title is uniformly distributed Uniform
description is uniformly distributed Uniform
director is uniformly distributed Uniform
actors is uniformly distributed Uniform
rank has unique values Unique
description has unique values Unique

Reproduction

Analysis started2021-01-30 16:31:40.415377
Analysis finished2021-01-30 16:31:54.978053
Duration14.56 seconds
Software versionpandas-profiling v2.10.0
Download configurationconfig.yaml

Variables

rank
Real number (ℝ≥0)

UNIFORM
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean500.5
Minimum1
Maximum1000
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-01-30T22:01:55.290026image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile50.95
Q1250.75
median500.5
Q3750.25
95-th percentile950.05
Maximum1000
Range999
Interquartile range (IQR)499.5

Descriptive statistics

Standard deviation288.8194361
Coefficient of variation (CV)0.5770618104
Kurtosis-1.2
Mean500.5
Median Absolute Deviation (MAD)250
Skewness0
Sum500500
Variance83416.66667
MonotocityStrictly increasing
2021-01-30T22:01:55.673556image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10001
 
0.1%
3291
 
0.1%
3421
 
0.1%
3411
 
0.1%
3401
 
0.1%
3391
 
0.1%
3381
 
0.1%
3371
 
0.1%
3361
 
0.1%
3351
 
0.1%
Other values (990)990
99.0%
ValueCountFrequency (%)
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
ValueCountFrequency (%)
10001
0.1%
9991
0.1%
9981
0.1%
9971
0.1%
9961
0.1%

title
Categorical

HIGH CARDINALITY
UNIFORM

Distinct999
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
The Host
 
2
The Invitation
 
1
La tortue rouge
 
1
Big Hero 6
 
1
Relatos salvajes
 
1
Other values (994)
994 

Length

Max length61
Median length13
Mean length14.539
Min length2

Characters and Unicode

Total characters14539
Distinct characters81
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique998 ?
Unique (%)99.8%

Sample

1st rowGuardians of the Galaxy
2nd rowPrometheus
3rd rowSplit
4th rowSing
5th rowSuicide Squad
ValueCountFrequency (%)
The Host2
 
0.2%
The Invitation1
 
0.1%
La tortue rouge1
 
0.1%
Big Hero 61
 
0.1%
Relatos salvajes1
 
0.1%
True Crimes1
 
0.1%
Steve Jobs1
 
0.1%
It Follows1
 
0.1%
Superbad1
 
0.1%
Legend1
 
0.1%
Other values (989)989
98.9%
2021-01-30T22:01:56.473652image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the305
 
11.7%
of92
 
3.5%
a29
 
1.1%
and22
 
0.8%
222
 
0.8%
in22
 
0.8%
15
 
0.6%
to12
 
0.5%
man12
 
0.5%
me11
 
0.4%
Other values (1429)2063
79.2%

Most occurring characters

ValueCountFrequency (%)
1605
 
11.0%
e1507
 
10.4%
a884
 
6.1%
o851
 
5.9%
n828
 
5.7%
r799
 
5.5%
i775
 
5.3%
t720
 
5.0%
s609
 
4.2%
h539
 
3.7%
Other values (71)5422
37.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter10340
71.1%
Uppercase Letter2274
 
15.6%
Space Separator1605
 
11.0%
Other Punctuation171
 
1.2%
Decimal Number110
 
0.8%
Dash Punctuation31
 
0.2%
Open Punctuation4
 
< 0.1%
Close Punctuation4
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e1507
14.6%
a884
 
8.5%
o851
 
8.2%
n828
 
8.0%
r799
 
7.7%
i775
 
7.5%
t720
 
7.0%
s609
 
5.9%
h539
 
5.2%
l457
 
4.4%
Other values (22)2371
22.9%
ValueCountFrequency (%)
T350
15.4%
S188
 
8.3%
M141
 
6.2%
B133
 
5.8%
D125
 
5.5%
A115
 
5.1%
P110
 
4.8%
H105
 
4.6%
C104
 
4.6%
W100
 
4.4%
Other values (16)803
35.3%
ValueCountFrequency (%)
235
31.8%
317
15.5%
115
13.6%
015
13.6%
57
 
6.4%
47
 
6.4%
75
 
4.5%
63
 
2.7%
93
 
2.7%
83
 
2.7%
ValueCountFrequency (%)
:85
49.7%
'39
22.8%
.23
 
13.5%
,9
 
5.3%
&6
 
3.5%
!4
 
2.3%
?2
 
1.2%
/2
 
1.2%
·1
 
0.6%
ValueCountFrequency (%)
1605
100.0%
ValueCountFrequency (%)
-31
100.0%
ValueCountFrequency (%)
(4
100.0%
ValueCountFrequency (%)
)4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin12614
86.8%
Common1925
 
13.2%

Most frequent character per script

ValueCountFrequency (%)
e1507
 
11.9%
a884
 
7.0%
o851
 
6.7%
n828
 
6.6%
r799
 
6.3%
i775
 
6.1%
t720
 
5.7%
s609
 
4.8%
h539
 
4.3%
l457
 
3.6%
Other values (48)4645
36.8%
ValueCountFrequency (%)
1605
83.4%
:85
 
4.4%
'39
 
2.0%
235
 
1.8%
-31
 
1.6%
.23
 
1.2%
317
 
0.9%
115
 
0.8%
015
 
0.8%
,9
 
0.5%
Other values (13)51
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII14530
99.9%
None9
 
0.1%

Most frequent character per block

ValueCountFrequency (%)
1605
 
11.0%
e1507
 
10.4%
a884
 
6.1%
o851
 
5.9%
n828
 
5.7%
r799
 
5.5%
i775
 
5.3%
t720
 
5.0%
s609
 
4.2%
h539
 
3.7%
Other values (64)5413
37.3%
ValueCountFrequency (%)
é3
33.3%
è1
 
11.1%
ä1
 
11.1%
·1
 
11.1%
í1
 
11.1%
á1
 
11.1%
ç1
 
11.1%

genre
Categorical

HIGH CARDINALITY

Distinct207
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Action,Adventure,Sci-Fi
 
50
Drama
 
48
Comedy,Drama,Romance
 
35
Comedy
 
32
Drama,Romance
 
31
Other values (202)
804 

Length

Max length26
Median length20
Mean length18.095
Min length5

Characters and Unicode

Total characters18095
Distinct characters31
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)8.5%

Sample

1st rowAction,Adventure,Sci-Fi
2nd rowAdventure,Mystery,Sci-Fi
3rd rowHorror,Thriller
4th rowAnimation,Comedy,Family
5th rowAction,Adventure,Fantasy
ValueCountFrequency (%)
Action,Adventure,Sci-Fi50
 
5.0%
Drama48
 
4.8%
Comedy,Drama,Romance35
 
3.5%
Comedy32
 
3.2%
Drama,Romance31
 
3.1%
Animation,Adventure,Comedy27
 
2.7%
Comedy,Drama27
 
2.7%
Action,Adventure,Fantasy27
 
2.7%
Comedy,Romance26
 
2.6%
Crime,Drama,Thriller24
 
2.4%
Other values (197)673
67.3%
2021-01-30T22:01:57.238242image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
action,adventure,sci-fi50
 
5.0%
drama48
 
4.8%
comedy,drama,romance35
 
3.5%
comedy32
 
3.2%
drama,romance31
 
3.1%
animation,adventure,comedy27
 
2.7%
action,adventure,fantasy27
 
2.7%
comedy,drama27
 
2.7%
comedy,romance26
 
2.6%
crime,drama,thriller24
 
2.4%
Other values (197)673
67.3%

Most occurring characters

ValueCountFrequency (%)
r1923
 
10.6%
a1568
 
8.7%
,1555
 
8.6%
e1403
 
7.8%
m1183
 
6.5%
i1168
 
6.5%
o1138
 
6.3%
n909
 
5.0%
t872
 
4.8%
y753
 
4.2%
Other values (21)5623
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter13745
76.0%
Uppercase Letter2675
 
14.8%
Other Punctuation1555
 
8.6%
Dash Punctuation120
 
0.7%

Most frequent character per category

ValueCountFrequency (%)
r1923
14.0%
a1568
11.4%
e1403
10.2%
m1183
8.6%
i1168
8.5%
o1138
8.3%
n909
6.6%
t872
 
6.3%
y753
 
5.5%
c585
 
4.3%
Other values (8)2243
16.3%
ValueCountFrequency (%)
A611
22.8%
D513
19.2%
C429
16.0%
F272
10.2%
T195
 
7.3%
H148
 
5.5%
R141
 
5.3%
S138
 
5.2%
M127
 
4.7%
B81
 
3.0%
ValueCountFrequency (%)
,1555
100.0%
ValueCountFrequency (%)
-120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin16420
90.7%
Common1675
 
9.3%

Most frequent character per script

ValueCountFrequency (%)
r1923
 
11.7%
a1568
 
9.5%
e1403
 
8.5%
m1183
 
7.2%
i1168
 
7.1%
o1138
 
6.9%
n909
 
5.5%
t872
 
5.3%
y753
 
4.6%
A611
 
3.7%
Other values (19)4892
29.8%
ValueCountFrequency (%)
,1555
92.8%
-120
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII18095
100.0%

Most frequent character per block

ValueCountFrequency (%)
r1923
 
10.6%
a1568
 
8.7%
,1555
 
8.6%
e1403
 
7.8%
m1183
 
6.5%
i1168
 
6.5%
o1138
 
6.3%
n909
 
5.0%
t872
 
4.8%
y753
 
4.2%
Other values (21)5623
31.1%

description
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct1000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
A tight-knit team of rising investigators, along with their supervisor, is suddenly torn apart when they discover that one of their own teenage daughters has been brutally murdered.
 
1
Three buddies wake up from a bachelor party in Las Vegas, with no memory of the previous night and the bachelor missing. They make their way around the city in order to find their friend before his wedding.
 
1
In 1984 East Berlin, an agent of the secret police, conducting surveillance on a writer and his lover, finds himself becoming increasingly absorbed by their lives.
 
1
After an experimental bio-weapon is released, turning thousands into zombie-like creatures, it's up to a rag-tag group of survivors to stop the infected and those behind its release.
 
1
Brian O'Conner, back working for the FBI in Los Angeles, teams up with Dominic Toretto to bring down a heroin importer by infiltrating his operation.
 
1
Other values (995)
995 

Length

Max length421
Median length159
Mean length163.232
Min length42

Characters and Unicode

Total characters163232
Distinct characters82
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1000 ?
Unique (%)100.0%

Sample

1st rowA group of intergalactic criminals are forced to work together to stop a fanatical warrior from taking control of the universe.
2nd rowFollowing clues to the origin of mankind, a team finds a structure on a distant moon, but they soon realize they are not alone.
3rd rowThree girls are kidnapped by a man with a diagnosed 23 distinct personalities. They must try to escape before the apparent emergence of a frightful new 24th.
4th rowIn a city of humanoid animals, a hustling theater impresario's attempt to save his theater with a singing competition becomes grander than he anticipates even as its finalists' find that their lives will never be the same.
5th rowA secret government agency recruits some of the most dangerous incarcerated super-villains to form a defensive task force. Their first mission: save the world from the apocalypse.
ValueCountFrequency (%)
A tight-knit team of rising investigators, along with their supervisor, is suddenly torn apart when they discover that one of their own teenage daughters has been brutally murdered.1
 
0.1%
Three buddies wake up from a bachelor party in Las Vegas, with no memory of the previous night and the bachelor missing. They make their way around the city in order to find their friend before his wedding.1
 
0.1%
In 1984 East Berlin, an agent of the secret police, conducting surveillance on a writer and his lover, finds himself becoming increasingly absorbed by their lives.1
 
0.1%
After an experimental bio-weapon is released, turning thousands into zombie-like creatures, it's up to a rag-tag group of survivors to stop the infected and those behind its release.1
 
0.1%
Brian O'Conner, back working for the FBI in Los Angeles, teams up with Dominic Toretto to bring down a heroin importer by infiltrating his operation.1
 
0.1%
A head chef quits his restaurant job and buys a food truck in an effort to reclaim his creative promise, while piecing back together his estranged family.1
 
0.1%
Newlywed couple Ted and Tami-Lynn want to have a baby, but in order to qualify to be a parent, Ted will have to prove he's a person in a court of law.1
 
0.1%
In an emotionless utopia, two people fall in love when they regain their feelings from a mysterious disease, causing tensions between them and their society.1
 
0.1%
When a member of a popular New York City improv troupe gets a huge break, the rest of the group - all best friends - start to realize that not everyone is going to make it after all.1
 
0.1%
A grief-stricken mother takes on the LAPD to her own detriment when it stubbornly tries to pass off an obvious impostor as her missing child, while also refusing to give up hope that she will find him one day.1
 
0.1%
Other values (990)990
99.0%
2021-01-30T22:01:57.784389image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a1626
 
5.8%
the1360
 
4.9%
to934
 
3.3%
of807
 
2.9%
and716
 
2.6%
in578
 
2.1%
his487
 
1.7%
an304
 
1.1%
is296
 
1.1%
with274
 
1.0%
Other values (6172)20539
73.6%

Most occurring characters

ValueCountFrequency (%)
26921
16.5%
e15840
 
9.7%
t10926
 
6.7%
a10686
 
6.5%
i9657
 
5.9%
o9618
 
5.9%
n9602
 
5.9%
r9227
 
5.7%
s8727
 
5.3%
h6513
 
4.0%
Other values (72)45515
27.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter128516
78.7%
Space Separator26921
 
16.5%
Uppercase Letter3786
 
2.3%
Other Punctuation2995
 
1.8%
Decimal Number506
 
0.3%
Dash Punctuation438
 
0.3%
Open Punctuation24
 
< 0.1%
Close Punctuation24
 
< 0.1%
Final Punctuation20
 
< 0.1%
Currency Symbol2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e15840
12.3%
t10926
 
8.5%
a10686
 
8.3%
i9657
 
7.5%
o9618
 
7.5%
n9602
 
7.5%
r9227
 
7.2%
s8727
 
6.8%
h6513
 
5.1%
l5169
 
4.0%
Other values (20)32551
25.3%
ValueCountFrequency (%)
A688
18.2%
T290
 
7.7%
S271
 
7.2%
B227
 
6.0%
W211
 
5.6%
C204
 
5.4%
I201
 
5.3%
M192
 
5.1%
F142
 
3.8%
H140
 
3.7%
Other values (16)1220
32.2%
ValueCountFrequency (%)
.1365
45.6%
,1216
40.6%
'297
 
9.9%
"66
 
2.2%
:26
 
0.9%
?11
 
0.4%
;8
 
0.3%
/4
 
0.1%
!1
 
< 0.1%
#1
 
< 0.1%
ValueCountFrequency (%)
1110
21.7%
0108
21.3%
992
18.2%
253
10.5%
731
 
6.1%
828
 
5.5%
625
 
4.9%
423
 
4.5%
523
 
4.5%
313
 
2.6%
ValueCountFrequency (%)
26921
100.0%
ValueCountFrequency (%)
-438
100.0%
ValueCountFrequency (%)
(24
100.0%
ValueCountFrequency (%)
)24
100.0%
ValueCountFrequency (%)
»20
100.0%
ValueCountFrequency (%)
$2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin132302
81.1%
Common30930
 
18.9%

Most frequent character per script

ValueCountFrequency (%)
e15840
12.0%
t10926
 
8.3%
a10686
 
8.1%
i9657
 
7.3%
o9618
 
7.3%
n9602
 
7.3%
r9227
 
7.0%
s8727
 
6.6%
h6513
 
4.9%
l5169
 
3.9%
Other values (46)36337
27.5%
ValueCountFrequency (%)
26921
87.0%
.1365
 
4.4%
,1216
 
3.9%
-438
 
1.4%
'297
 
1.0%
1110
 
0.4%
0108
 
0.3%
992
 
0.3%
"66
 
0.2%
253
 
0.2%
Other values (16)264
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII163203
> 99.9%
None29
 
< 0.1%

Most frequent character per block

ValueCountFrequency (%)
26921
16.5%
e15840
 
9.7%
t10926
 
6.7%
a10686
 
6.5%
i9657
 
5.9%
o9618
 
5.9%
n9602
 
5.9%
r9227
 
5.7%
s8727
 
5.3%
h6513
 
4.0%
Other values (67)45486
27.9%
ValueCountFrequency (%)
»20
69.0%
é4
 
13.8%
á2
 
6.9%
è2
 
6.9%
í1
 
3.4%

director
Categorical

HIGH CARDINALITY
UNIFORM

Distinct644
Distinct (%)64.4%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Ridley Scott
 
8
David Yates
 
6
Michael Bay
 
6
Paul W.S. Anderson
 
6
M. Night Shyamalan
 
6
Other values (639)
968 

Length

Max length32
Median length13
Mean length13.139
Min length3

Characters and Unicode

Total characters13139
Distinct characters69
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique444 ?
Unique (%)44.4%

Sample

1st rowJames Gunn
2nd rowRidley Scott
3rd rowM. Night Shyamalan
4th rowChristophe Lourdelet
5th rowDavid Ayer
ValueCountFrequency (%)
Ridley Scott8
 
0.8%
David Yates6
 
0.6%
Michael Bay6
 
0.6%
Paul W.S. Anderson6
 
0.6%
M. Night Shyamalan6
 
0.6%
Antoine Fuqua5
 
0.5%
Denis Villeneuve5
 
0.5%
Danny Boyle5
 
0.5%
Martin Scorsese5
 
0.5%
Zack Snyder5
 
0.5%
Other values (634)943
94.3%
2021-01-30T22:01:58.672241image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
david38
 
1.8%
john25
 
1.2%
michael22
 
1.1%
james21
 
1.0%
scott20
 
1.0%
paul19
 
0.9%
robert14
 
0.7%
steven13
 
0.6%
lee12
 
0.6%
peter12
 
0.6%
Other values (977)1896
90.6%

Most occurring characters

ValueCountFrequency (%)
e1223
 
9.3%
1092
 
8.3%
a1056
 
8.0%
n937
 
7.1%
r875
 
6.7%
o783
 
6.0%
i740
 
5.6%
l604
 
4.6%
t486
 
3.7%
s467
 
3.6%
Other values (59)4876
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter9802
74.6%
Uppercase Letter2153
 
16.4%
Space Separator1092
 
8.3%
Other Punctuation73
 
0.6%
Dash Punctuation19
 
0.1%

Most frequent character per category

ValueCountFrequency (%)
e1223
12.5%
a1056
10.8%
n937
9.6%
r875
 
8.9%
o783
 
8.0%
i740
 
7.5%
l604
 
6.2%
t486
 
5.0%
s467
 
4.8%
h357
 
3.6%
Other values (28)2274
23.2%
ValueCountFrequency (%)
S207
 
9.6%
J200
 
9.3%
M183
 
8.5%
A148
 
6.9%
D137
 
6.4%
G131
 
6.1%
B127
 
5.9%
C123
 
5.7%
R119
 
5.5%
L108
 
5.0%
Other values (17)670
31.1%
ValueCountFrequency (%)
.71
97.3%
'2
 
2.7%
ValueCountFrequency (%)
1092
100.0%
ValueCountFrequency (%)
-19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin11955
91.0%
Common1184
 
9.0%

Most frequent character per script

ValueCountFrequency (%)
e1223
 
10.2%
a1056
 
8.8%
n937
 
7.8%
r875
 
7.3%
o783
 
6.5%
i740
 
6.2%
l604
 
5.1%
t486
 
4.1%
s467
 
3.9%
h357
 
3.0%
Other values (55)4427
37.0%
ValueCountFrequency (%)
1092
92.2%
.71
 
6.0%
-19
 
1.6%
'2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII13095
99.7%
None44
 
0.3%

Most frequent character per block

ValueCountFrequency (%)
e1223
 
9.3%
1092
 
8.3%
a1056
 
8.1%
n937
 
7.2%
r875
 
6.7%
o783
 
6.0%
i740
 
5.7%
l604
 
4.6%
t486
 
3.7%
s467
 
3.6%
Other values (46)4832
36.9%
ValueCountFrequency (%)
é11
25.0%
á9
20.5%
ó4
 
9.1%
ö4
 
9.1%
å4
 
9.1%
ñ3
 
6.8%
ç3
 
6.8%
Ø1
 
2.3%
í1
 
2.3%
ë1
 
2.3%
Other values (3)3
 
6.8%

actors
Categorical

HIGH CARDINALITY
UNIFORM

Distinct996
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Memory size4.0 KiB
Gerard Butler, Aaron Eckhart, Morgan Freeman,Angela Bassett
 
2
Shia LaBeouf, Megan Fox, Josh Duhamel, Tyrese Gibson
 
2
Daniel Radcliffe, Emma Watson, Rupert Grint, Michael Gambon
 
2
Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Woody Harrelson
 
2
Shia LaBeouf, David Morse, Carrie-Anne Moss, Sarah Roemer
 
1
Other values (991)
991 

Length

Max length77
Median length58
Mean length58.288
Min length43

Characters and Unicode

Total characters58288
Distinct characters79
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique992 ?
Unique (%)99.2%

Sample

1st rowChris Pratt, Vin Diesel, Bradley Cooper, Zoe Saldana
2nd rowNoomi Rapace, Logan Marshall-Green, Michael Fassbender, Charlize Theron
3rd rowJames McAvoy, Anya Taylor-Joy, Haley Lu Richardson, Jessica Sula
4th rowMatthew McConaughey,Reese Witherspoon, Seth MacFarlane, Scarlett Johansson
5th rowWill Smith, Jared Leto, Margot Robbie, Viola Davis
ValueCountFrequency (%)
Gerard Butler, Aaron Eckhart, Morgan Freeman,Angela Bassett2
 
0.2%
Shia LaBeouf, Megan Fox, Josh Duhamel, Tyrese Gibson2
 
0.2%
Daniel Radcliffe, Emma Watson, Rupert Grint, Michael Gambon2
 
0.2%
Jennifer Lawrence, Josh Hutcherson, Liam Hemsworth, Woody Harrelson2
 
0.2%
Shia LaBeouf, David Morse, Carrie-Anne Moss, Sarah Roemer1
 
0.1%
Shailene Woodley, Ansel Elgort, Nat Wolff, Laura Dern1
 
0.1%
Adam Sandler, Jennifer Aniston, Brooklyn Decker,Nicole Kidman1
 
0.1%
Willem Dafoe, Charlotte Gainsbourg, Storm Acheche Sahlstrøm1
 
0.1%
Steve Carell, Ryan Gosling, Julianne Moore, Emma Stone1
 
0.1%
Tom Hardy, Noomi Rapace, James Gandolfini,Matthias Schoenaerts1
 
0.1%
Other values (986)986
98.6%
2021-01-30T22:01:59.220458image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michael62
 
0.8%
james50
 
0.6%
tom44
 
0.6%
chris42
 
0.5%
john42
 
0.5%
jason41
 
0.5%
robert37
 
0.5%
mark35
 
0.4%
jennifer35
 
0.4%
ben31
 
0.4%
Other values (2924)7441
94.7%

Most occurring characters

ValueCountFrequency (%)
6860
 
11.8%
e5007
 
8.6%
a4812
 
8.3%
n3867
 
6.6%
i3216
 
5.5%
r3171
 
5.4%
,2999
 
5.1%
o2949
 
5.1%
l2811
 
4.8%
s1931
 
3.3%
Other values (69)20665
35.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter39800
68.3%
Uppercase Letter8428
 
14.5%
Space Separator6860
 
11.8%
Other Punctuation3117
 
5.3%
Dash Punctuation81
 
0.1%
Decimal Number2
 
< 0.1%

Most frequent character per category

ValueCountFrequency (%)
e5007
12.6%
a4812
12.1%
n3867
9.7%
i3216
 
8.1%
r3171
 
8.0%
o2949
 
7.4%
l2811
 
7.1%
s1931
 
4.9%
t1924
 
4.8%
h1557
 
3.9%
Other values (33)8555
21.5%
ValueCountFrequency (%)
J749
 
8.9%
M725
 
8.6%
C661
 
7.8%
S632
 
7.5%
B618
 
7.3%
A520
 
6.2%
R507
 
6.0%
D473
 
5.6%
L389
 
4.6%
H385
 
4.6%
Other values (19)2769
32.9%
ValueCountFrequency (%)
,2999
96.2%
.91
 
2.9%
'27
 
0.9%
ValueCountFrequency (%)
51
50.0%
01
50.0%
ValueCountFrequency (%)
6860
100.0%
ValueCountFrequency (%)
-81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin48228
82.7%
Common10060
 
17.3%

Most frequent character per script

ValueCountFrequency (%)
e5007
 
10.4%
a4812
 
10.0%
n3867
 
8.0%
i3216
 
6.7%
r3171
 
6.6%
o2949
 
6.1%
l2811
 
5.8%
s1931
 
4.0%
t1924
 
4.0%
h1557
 
3.2%
Other values (62)16983
35.2%
ValueCountFrequency (%)
6860
68.2%
,2999
29.8%
.91
 
0.9%
-81
 
0.8%
'27
 
0.3%
51
 
< 0.1%
01
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII58177
99.8%
None111
 
0.2%

Most frequent character per block

ValueCountFrequency (%)
6860
 
11.8%
e5007
 
8.6%
a4812
 
8.3%
n3867
 
6.6%
i3216
 
5.5%
r3171
 
5.5%
,2999
 
5.2%
o2949
 
5.1%
l2811
 
4.8%
s1931
 
3.3%
Other values (49)20554
35.3%
ValueCountFrequency (%)
é29
26.1%
ë16
14.4%
á12
10.8%
í10
 
9.0%
å10
 
9.0%
ü6
 
5.4%
ñ5
 
4.5%
è4
 
3.6%
Ó3
 
2.7%
ô2
 
1.8%
Other values (10)14
12.6%

year
Real number (ℝ≥0)

Distinct11
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.783
Minimum2006
Maximum2016
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-01-30T22:01:59.426545image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum2006
5-th percentile2007
Q12010
median2014
Q32016
95-th percentile2016
Maximum2016
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.205961508
Coefficient of variation (CV)0.00159280037
Kurtosis-0.8219639755
Mean2012.783
Median Absolute Deviation (MAD)2
Skewness-0.6898787091
Sum2012783
Variance10.27818919
MonotocityNot monotonic
2021-01-30T22:01:59.626727image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2016297
29.7%
2015127
12.7%
201498
 
9.8%
201391
 
9.1%
201264
 
6.4%
201163
 
6.3%
201060
 
6.0%
200753
 
5.3%
200852
 
5.2%
200951
 
5.1%
ValueCountFrequency (%)
200644
4.4%
200753
5.3%
200852
5.2%
200951
5.1%
201060
6.0%
ValueCountFrequency (%)
2016297
29.7%
2015127
12.7%
201498
 
9.8%
201391
 
9.1%
201264
 
6.4%

runtime (minutes)
Real number (ℝ≥0)

Distinct94
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean113.172
Minimum66
Maximum191
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-01-30T22:01:59.927579image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum66
5-th percentile88
Q1100
median111
Q3123
95-th percentile150
Maximum191
Range125
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.81090817
Coefficient of variation (CV)0.1662152138
Kurtosis0.8583211032
Mean113.172
Median Absolute Deviation (MAD)12
Skewness0.8467127314
Sum113172
Variance353.8502663
MonotocityNot monotonic
2021-01-30T22:02:00.255677image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10831
 
3.1%
10028
 
2.8%
11727
 
2.7%
11026
 
2.6%
10626
 
2.6%
11826
 
2.6%
10225
 
2.5%
11224
 
2.4%
10423
 
2.3%
12323
 
2.3%
Other values (84)741
74.1%
ValueCountFrequency (%)
661
 
0.1%
732
 
0.2%
802
 
0.2%
815
0.5%
821
 
0.1%
ValueCountFrequency (%)
1911
 
0.1%
1871
 
0.1%
1803
0.3%
1721
 
0.1%
1701
 
0.1%

rating
Real number (ℝ≥0)

Distinct55
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.7232
Minimum1.9
Maximum9
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-01-30T22:02:00.561970image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum1.9
5-th percentile5.1
Q16.2
median6.8
Q37.4
95-th percentile8.1
Maximum9
Range7.1
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.9454287893
Coefficient of variation (CV)0.1406218451
Kurtosis1.322270288
Mean6.7232
Median Absolute Deviation (MAD)0.6
Skewness-0.7431419408
Sum6723.2
Variance0.8938355956
MonotocityNot monotonic
2021-01-30T22:02:00.818404image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.152
 
5.2%
6.748
 
4.8%
746
 
4.6%
6.344
 
4.4%
6.642
 
4.2%
7.242
 
4.2%
7.342
 
4.2%
6.540
 
4.0%
7.840
 
4.0%
6.237
 
3.7%
Other values (45)567
56.7%
ValueCountFrequency (%)
1.91
0.1%
2.72
0.2%
3.21
0.1%
3.52
0.2%
3.72
0.2%
ValueCountFrequency (%)
91
 
0.1%
8.82
 
0.2%
8.63
0.3%
8.56
0.6%
8.44
0.4%

votes
Real number (ℝ≥0)

Distinct997
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean169808.255
Minimum61
Maximum1791916
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-01-30T22:02:01.068386image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile1260.35
Q136309
median110799
Q3239909.75
95-th percentile526551.85
Maximum1791916
Range1791855
Interquartile range (IQR)203600.75

Descriptive statistics

Standard deviation188762.6475
Coefficient of variation (CV)1.111622327
Kurtosis11.3126809
Mean169808.255
Median Absolute Deviation (MAD)88402
Skewness2.507918483
Sum169808255
Variance3.56313371 × 1010
MonotocityNot monotonic
2021-01-30T22:02:01.315127image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14272
 
0.2%
971412
 
0.2%
2912
 
0.2%
5311121
 
0.1%
7021
 
0.1%
478041
 
0.1%
2266191
 
0.1%
764691
 
0.1%
1256931
 
0.1%
1745531
 
0.1%
Other values (987)987
98.7%
ValueCountFrequency (%)
611
0.1%
961
0.1%
1021
0.1%
1151
0.1%
1641
0.1%
ValueCountFrequency (%)
17919161
0.1%
15836251
0.1%
12226451
0.1%
10477471
0.1%
10455881
0.1%

revenue (millions)
Real number (ℝ≥0)

MISSING

Distinct814
Distinct (%)93.3%
Missing128
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean82.95637615
Minimum0
Maximum936.63
Zeros1
Zeros (%)0.1%
Memory size7.9 KiB
2021-01-30T22:02:01.572285image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.211
Q113.27
median47.985
Q3113.715
95-th percentile293.88
Maximum936.63
Range936.63
Interquartile range (IQR)100.445

Descriptive statistics

Standard deviation103.2535405
Coefficient of variation (CV)1.244672746
Kurtosis10.60763453
Mean82.95637615
Median Absolute Deviation (MAD)41.285
Skewness2.592515866
Sum72337.96
Variance10661.29362
MonotocityNot monotonic
2021-01-30T22:02:01.806628image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.037
 
0.7%
0.015
 
0.5%
0.044
 
0.4%
0.024
 
0.4%
0.324
 
0.4%
0.054
 
0.4%
1.293
 
0.3%
0.153
 
0.3%
2.23
 
0.3%
0.543
 
0.3%
Other values (804)832
83.2%
(Missing)128
 
12.8%
ValueCountFrequency (%)
01
 
0.1%
0.015
0.5%
0.024
0.4%
0.037
0.7%
0.044
0.4%
ValueCountFrequency (%)
936.631
0.1%
760.511
0.1%
652.181
0.1%
623.281
0.1%
533.321
0.1%

metascore
Real number (ℝ≥0)

MISSING

Distinct84
Distinct (%)9.0%
Missing64
Missing (%)6.4%
Infinite0
Infinite (%)0.0%
Mean58.98504274
Minimum11
Maximum100
Zeros0
Zeros (%)0.0%
Memory size7.9 KiB
2021-01-30T22:02:02.048319image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile31
Q147
median59.5
Q372
95-th percentile85
Maximum100
Range89
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.19475702
Coefficient of variation (CV)0.2915104614
Kurtosis-0.6122051468
Mean58.98504274
Median Absolute Deviation (MAD)12.5
Skewness-0.1238873467
Sum55210
Variance295.6596691
MonotocityNot monotonic
2021-01-30T22:02:02.267054image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6625
 
2.5%
7225
 
2.5%
6825
 
2.5%
6424
 
2.4%
5723
 
2.3%
5122
 
2.2%
6522
 
2.2%
4821
 
2.1%
8121
 
2.1%
7621
 
2.1%
Other values (74)707
70.7%
(Missing)64
 
6.4%
ValueCountFrequency (%)
111
 
0.1%
151
 
0.1%
161
 
0.1%
184
0.4%
191
 
0.1%
ValueCountFrequency (%)
1001
 
0.1%
991
 
0.1%
981
 
0.1%
964
0.4%
953
0.3%

Interactions

2021-01-30T22:01:41.792128image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:42.023339image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:42.284807image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:42.546130image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:42.800266image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:43.032533image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:43.237243image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:43.497112image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:43.803422image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:44.093002image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:44.374249image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:44.624231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:44.842952image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:45.077326image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:45.316840image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:45.560035image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:45.828413image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:46.040743image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:46.275099image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:46.526613image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:46.760955image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:47.010953image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:47.339043image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:47.742331image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:48.023651image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:48.320501image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:48.675175image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:49.031231image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:49.419605image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:49.700836image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:50.016715image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:50.282313image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:50.532297image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:50.797902image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:51.063508image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:51.361227image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:51.621814image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:51.887422image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:52.137403image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:52.417534image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:52.691383image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
2021-01-30T22:01:52.980010image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Correlations

2021-01-30T22:02:02.470150image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-01-30T22:02:02.777453image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-01-30T22:02:03.046482image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-01-30T22:02:03.327344image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-01-30T22:01:53.538696image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-01-30T22:01:54.131943image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-01-30T22:01:54.494538image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-01-30T22:01:54.715627image/svg+xmlMatplotlib v3.3.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

ranktitlegenredescriptiondirectoractorsyearruntime (minutes)ratingvotesrevenue (millions)metascore
01Guardians of the GalaxyAction,Adventure,Sci-FiA group of intergalactic criminals are forced to work together to stop a fanatical warrior from taking control of the universe.James GunnChris Pratt, Vin Diesel, Bradley Cooper, Zoe Saldana20141218.1757074333.1376.0
12PrometheusAdventure,Mystery,Sci-FiFollowing clues to the origin of mankind, a team finds a structure on a distant moon, but they soon realize they are not alone.Ridley ScottNoomi Rapace, Logan Marshall-Green, Michael Fassbender, Charlize Theron20121247.0485820126.4665.0
23SplitHorror,ThrillerThree girls are kidnapped by a man with a diagnosed 23 distinct personalities. They must try to escape before the apparent emergence of a frightful new 24th.M. Night ShyamalanJames McAvoy, Anya Taylor-Joy, Haley Lu Richardson, Jessica Sula20161177.3157606138.1262.0
34SingAnimation,Comedy,FamilyIn a city of humanoid animals, a hustling theater impresario's attempt to save his theater with a singing competition becomes grander than he anticipates even as its finalists' find that their lives will never be the same.Christophe LourdeletMatthew McConaughey,Reese Witherspoon, Seth MacFarlane, Scarlett Johansson20161087.260545270.3259.0
45Suicide SquadAction,Adventure,FantasyA secret government agency recruits some of the most dangerous incarcerated super-villains to form a defensive task force. Their first mission: save the world from the apocalypse.David AyerWill Smith, Jared Leto, Margot Robbie, Viola Davis20161236.2393727325.0240.0
56The Great WallAction,Adventure,FantasyEuropean mercenaries searching for black powder become embroiled in the defense of the Great Wall of China against a horde of monstrous creatures.Yimou ZhangMatt Damon, Tian Jing, Willem Dafoe, Andy Lau20161036.15603645.1342.0
67La La LandComedy,Drama,MusicA jazz pianist falls for an aspiring actress in Los Angeles.Damien ChazelleRyan Gosling, Emma Stone, Rosemarie DeWitt, J.K. Simmons20161288.3258682151.0693.0
78MindhornComedyA has-been actor best known for playing the title character in the 1980s detective series "Mindhorn" must work with the police when a serial killer says that he will only speak with Detective Mindhorn, whom he believes to be a real person.Sean FoleyEssie Davis, Andrea Riseborough, Julian Barratt,Kenneth Branagh2016896.42490NaN71.0
89The Lost City of ZAction,Adventure,BiographyA true-life drama, centering on British explorer Col. Percival Fawcett, who disappeared while searching for a mysterious city in the Amazon in the 1920s.James GrayCharlie Hunnam, Robert Pattinson, Sienna Miller, Tom Holland20161417.171888.0178.0
910PassengersAdventure,Drama,RomanceA spacecraft traveling to a distant colony planet and transporting thousands of people has a malfunction in its sleep chambers. As a result, two passengers are awakened 90 years early.Morten TyldumJennifer Lawrence, Chris Pratt, Michael Sheen,Laurence Fishburne20161167.0192177100.0141.0

Last rows

ranktitlegenredescriptiondirectoractorsyearruntime (minutes)ratingvotesrevenue (millions)metascore
990991Underworld: Rise of the LycansAction,Adventure,FantasyAn origins story centered on the centuries-old feud between the race of aristocratic vampires and their onetime slaves, the Lycans.Patrick TatopoulosRhona Mitra, Michael Sheen, Bill Nighy, Steven Mackintosh2009926.612970845.8044.0
991992Taare Zameen ParDrama,Family,MusicAn eight-year-old boy is thought to be a lazy trouble-maker, until the new art teacher has the patience and compassion to discover the real problem behind his struggles in school.Aamir KhanDarsheel Safary, Aamir Khan, Tanay Chheda, Sachet Engineer20071658.51026971.2042.0
992993Take Me Home TonightComedy,Drama,RomanceFour years after graduation, an awkward high school genius uses his sister's boyfriend's Labor Day party as the perfect opportunity to make his move on his high school crush.Michael DowseTopher Grace, Anna Faris, Dan Fogler, Teresa Palmer2011976.3454196.92NaN
993994Resident Evil: AfterlifeAction,Adventure,HorrorWhile still out to destroy the evil Umbrella Corporation, Alice joins a group of survivors living in a prison surrounded by the infected who also want to relocate to the mysterious but supposedly unharmed safe haven known only as Arcadia.Paul W.S. AndersonMilla Jovovich, Ali Larter, Wentworth Miller,Kim Coates2010975.914090060.1337.0
994995Project XComedy3 high school seniors throw a birthday party to make a name for themselves. As the night progresses, things spiral out of control as word of the party spreads.Nima NourizadehThomas Mann, Oliver Cooper, Jonathan Daniel Brown, Dax Flame2012886.716408854.7248.0
995996Secret in Their EyesCrime,Drama,MysteryA tight-knit team of rising investigators, along with their supervisor, is suddenly torn apart when they discover that one of their own teenage daughters has been brutally murdered.Billy RayChiwetel Ejiofor, Nicole Kidman, Julia Roberts, Dean Norris20151116.227585NaN45.0
996997Hostel: Part IIHorrorThree American college students studying abroad are lured to a Slovakian hostel, and discover the grim reality behind it.Eli RothLauren German, Heather Matarazzo, Bijou Phillips, Roger Bart2007945.57315217.5446.0
997998Step Up 2: The StreetsDrama,Music,RomanceRomantic sparks occur between two dance students from different backgrounds at the Maryland School of the Arts.Jon M. ChuRobert Hoffman, Briana Evigan, Cassie Ventura, Adam G. Sevani2008986.27069958.0150.0
998999Search PartyAdventure,ComedyA pair of friends embark on a mission to reunite their pal with the woman he was going to marry.Scot ArmstrongAdam Pally, T.J. Miller, Thomas Middleditch,Shannon Woodward2014935.64881NaN22.0
9991000Nine LivesComedy,Family,FantasyA stuffy businessman finds himself trapped inside the body of his family's cat.Barry SonnenfeldKevin Spacey, Jennifer Garner, Robbie Amell,Cheryl Hines2016875.31243519.6411.0